Differential Controls Over Tactile Detection in Humans by Motor Commands and Peripheral Reafference
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this study was to determine the extent to which motor commands and peripheral reafference differentially control the detection of near-threshold, tactile stimuli. Detection of weak electrical stimuli applied to the index finger (D2) was evaluated with two bias-free measures of sensory detection, the index of detectability (d') and the proportion of stimuli detected. Stimuli were presented at different delays prior to and during two motor tasks, D2 abduction, and elbow extension; both tasks were tested in two modes, active and passive. For both active tasks, the peak decrease in tactile suppression occurred at the onset of electromyographic activity. The time course for the suppression of detection during active and passive D2 abduction was identical, and preceded the onset of movement (respectively, -35 and -47 ms). These results suggest that movement reafference alone, acting through a mechanism of backward masking, could explain the modulation seen with D2 movement. In contrast, tactile suppression was significantly earlier for active elbow movements (-59 ms) as compared with passive (-21 ms), an observation consistent with both the motor command and peripheral reafference contributing to the suppression of detection of stimuli applied to D2 during movements about a proximal joint. A role for the motor command in tactile gating during distal movements cannot be discounted, however, because differences in the strength and distribution of the peripheral reafference may also have contributed to the proximo-distal differences in the timing of the suppression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it